Integrating distribution models and habitat classification maps into marine protected area planning. (15th November 2018)

• Record Type: Journal Article
• Authors: Ferrari, Renata; Malcolm, Hamish; Neilson, Joe; Lucieer, Vanessa; Jordan, Alan; Ingleton, Tim; Figueira, Will; Johnstone, Nicola; Hill, Nicole
• Is Part Of: Estuarine, coastal and shelf science. Volume 212(2018)
• Page Start: 40
• Page End: 50
• Publication Date: 2018-11-15
• Subjects: Ecological and functional surrogates -- Endemic and threatened species -- Fish -- Habitat classification scheme -- Habitat maps -- Stakeholders -- Local knowledge and expertise -- Marine protected areas -- Marxan -- Conservation prioritization -- Spatial management -- Conservation planning -- Data-deficient ecosystems; Estuarine oceanography -- Periodicals; Coasts -- Periodicals; Estuarine biology -- Periodicals; Seashore biology -- Periodicals; Coasts; Estuarine biology; Estuarine oceanography; Seashore biology; Periodicals; 551.461805
• DOI: 10.1016/j.ecss.2018.06.015
• Languages: English
• ISSNs: 0272-7714

Abstract

Abstract: Effective conservation planning requires biotic data across an entire region. In data-poor ecosystems conservation planning is informed by using environmental surrogates ( e.g. temperature) predominantly in two ways: to develop habitat classification schemes (1) or develop species distribution models (2). We test the utility of both approaches for conservation planning of marine ecosystems, and rank environmental surrogates, such as depth and distance from shore, according to their power to predict the distribution and abundance of biotic species. Specifically, we compared a habitat classification scheme; based on coarse levels of habitat types derived from depth and distance from shore; against species distribution models, which predict fish abundance and prevalence as a function of environmental surrogates (depth, distance from shore, latitude, reef area, zoning, and several metrics of habitat structural complexity). We consistently set conservation target levels to 21% of each conservation feature, following global standards and a sensitivity analyses. Thus when running scenarios to protect fish species we aimed to protect at least 21% of each species, and when running scenarios of habitat classes, we aimed to protect at least 21% of each habitat class. We found that when aiming to protect 21% of the chosen conservation targets, distribution models protected 21% of the predicted abundance/occurrence of all modelled species and functional groups, but did not protect most habitats. Contrastingly, using a habitat classification scheme protected 21% of all habitat types and 34% of all species and functional groups, but required protecting three times more area. Thus, using only distribution models as targets in data-poor ecosystems could be a risky conservation planning strategy. Ultimately the best conservation outcomes were achieved by incorporating local knowledge to synthesize the conservation outcomes of both scenarios.